Summary of Exploring the Task-agnostic Trait Of Self-supervised Learning in the Context Of Detecting Mental Disorders, by Rohan Kumar Gupta and Rohit Sinha
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders
by Rohan Kumar Gupta, Rohit Sinha
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study explores self-supervised learning (SSL) in detecting major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) using audio and video data from interactive sessions. SSL models trained on predicting multiple fixed targets or masked frames are employed, with hyper-parameters modified to generate global representations that capture varying temporal contexts. The innovations yield improved detection performances for the considered mental disorders and exhibit task-agnostic traits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special learning methods called self-supervised learning to help computers detect two mental health issues: major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). It uses audio and video recordings from conversations with people who have these conditions. The goal is to create a way for computers to understand what’s happening in the recordings, so they can better detect when someone has MDD or PTSD. |
Keywords
* Artificial intelligence * Self supervised
